Message Security Level Integration with IoTES: A Design Dependent Encryption Selection Model for IoT Devices

Saleh, Matasem,Jhanjhi, NZ,Abdullah, Azween,Saher, Raazia International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.8

The Internet of Things (IoT) is a technology that offers lucrative services in various industries to facilitate human communities. Important information on people and their surroundings has been gathered to ensure the availability of these services. This data is vulnerable to cybersecurity since it is sent over the internet and kept in third-party databases. Implementation of data encryption is an integral approach for IoT device designers to protect IoT data. For a variety of reasons, IoT device designers have been unable to discover appropriate encryption to use. The static support provided by research and concerned organizations to assist designers in picking appropriate encryption costs a significant amount of time and effort. IoTES is a web app that uses machine language to address a lack of support from researchers and organizations, as ML has been shown to improve data-driven human decision-making. IoTES still has some weaknesses, which are highlighted in this research. To improve the support, these shortcomings must be addressed. This study proposes the "IoTES with Security" model by adding support for the security level provided by the encryption algorithm to the traditional IoTES model. We evaluated our technique for encryption algorithms with available security levels and compared the accuracy of our model with traditional IoTES. Our model improves IoTES by helping users make security-oriented decisions while choosing the appropriate algorithm for their IoT data.

Combination of Brain Cancer with Hybrid K-NN Algorithm using Statistical of Cerebrospinal Fluid (CSF) Surgery

Saeed, Soobia,Abdullah, Afnizanfaizal,Jhanjhi, NZ International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.2

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

The Smart Medicine Delivery Using UAV for Elderly Center

Li, Jie,Weiwei, Goh,N.Z., Jhanjhi,David, Asirvatham International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.1

Medication safety and medicine delivery challenge the well-being of the elderly and the management of the elderly center. With the outbreak of COVID-19, the elderly in the care center were challenged by the inconvenience of the medication restocking. The purpose of this paper accentuates the importance of the design and development of an UAV-based Smart Medicine Case (UAV-SMC) to improve the performance of medication management and medicine delivery in the elderly center. The researchers came up with the design of UAV-SMC in the light of the UAV and IoT technology to improve the performance of both Medication Practice Management (MPM) and Low Inventory Detection and Delivery (LIDD). Based on the result, with UAV-SMC, the performance of both MPM and LIDD was significantly improved. The UAV-SMC improves the efficacy of medication management in the elderly center by 26.97 to 149.83 seconds for each medication practice and 9.03 mins for each time of medicine delivery in Subang Jaya Malaysia. This paper only investigates the adoption of UAV-SMC in the content of elderly center rather than other industries. The authors consider integrating the UAV-SMC with the e-pharmacy system in the future. In conclusion, the UAV-SMC has significantly improved the medication management and guard the safety of elderly and caretaker in the elderly in the post-pandemic times.

Analyzing the Impact of Social Distancing on the Stoning Ritual of the Islamic Pilgrimage

Qazi Mudassar Ilya,Muneer Ahmad,Noor Zaman Jhanjhi,Muhammad Bilal Ahmad 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.6

The COVID-19 pandemic has resulted in a profound impact on large-scale gatherings throughout the world. Social distancing has become one of the most common measures to restrict the spread of the novel Coronavirus. Islamic pilgrimage attracts millions of pilgrims to Saudi Arabia annually. One of the mandatory rituals of pilgrimage is the symbolic stoning of the devil. Every pilgrim is required to perform this ritual within a specified time on three days of pilgrimage. This ritual is prone to congestion due to strict spatiotemporal requirements. We propose a pedestrian simulation model for implementing social distancing in the stoning ritual. An agent-based simulation is designed to analyze the impact of inter-queue and intra-queue spacing between adjacent pilgrims on the throughput and congestion during the stoning ritual. After analyzing several combinations of intra-queue and inter-queue spacings, we conclude that 25 queues with 1.5 meters of intra-queue spacing result in an optimal combination of throughput and congestion. The Ministry of Hajj in Saudi Arabia may benefit from these findings to manage and plan pilgrimage more effectively.

Bargaining based Design Mechanism for delay sensitive tasks of mobile crowdsensing in IoT

Ahmad Waqas,Ullah Ata,Sheharyar,Jhanjhi N.Z.,Ghoniem Rania M.,Brohi Sarfraz Nawaz 한국통신학회 2024 ICT Express Vol.10 No.1

Internet of Things (IoT) is getting growing interest to offer great opportunities in combination with Mobile Crowd Sensing for real-time applications. Existing approaches motivate mobile workers (MWs) for approaching the distant locations to receive attractive incentives for traveling. The main question addressed is that a number of tasks remain incomplete out of total al-located tasks. Moreover, the profitability and feasible budget constraints of the platform is also not considered. This paper presents Bargaining based Design Mechanism (BDM) to involve the nearest located MWs to improve the completion of tasks. The main method involves a bargaining based game model that increases the task completion ratio while considering the feasible budget constraint, platform profitability and social welfare. The proposed approach comprises of two algorithms: one for the selection of optimal MWs with low cost and less delay. Second is to organize bargaining for rewarding the platform on social welfare. Our work is validated by developing a testbed on Windows Azure cloud. Results prove that proposed BDM out-performs the counterparts in terms of decay coefficient, task completion ratio, participant’s winning ratio, fraction of task incompletion and social welfare.

Optimize KNN Algorithm for Cerebrospinal Fluid Cell Diseases

Soobia Saeed,Afnizanfaizal Abdullah,NZ Jhanjhi International Journal of Computer ScienceNetwork S 2024 International journal of computer science and netw Vol.24 No.2

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.

Cyber Security Attacks and Challenges in Saudi Arabia during COVID-19

Nourah Almrezeq,Mamoona Humayun,Madallah Alruwaili,Saad Alanazi,NZ Jhanjhi International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.10

The outbreak of COVID-19 had affected almost every part of the world and caused disastrous results, the number of reported COVID-19 cases in past few months have reached to more than 29 million patients in the world globally. This pandemic has adversely affected all the activities of life, ranging from personal life to overall economic development. Due to the current situation, routinely turned to online resources, and people have relied on technology more than they have been before. Since cybercriminals are an opportunist and they utilized this entirely, by targeting the online services for all sectors of life. This fortnight online dependency of the community over the internet opened several easy doors for the cybercriminals. This causes exponential attacks over internet traffic during this epidemic situation. The current Covid-19 pandemic situation appeared at once, and no one was ready to prevail this. However, there is an urgent need to address the current problem in all means. . KSA is among one of the countries most affected by these CA and is a key victim for most cyber-crimes. Therefore, this paper will review the effects of COVID-19 on the cyber-world of KSA in various sectors. We will also shed light on the Saudi efforts to confront these attacks during COVID -19. As a contribution, we have provided a comprehensive framework for mitigating cybersecurity challenges.